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Spatially resolved transcriptomics (SRT) provides an invaluable avenue for examining cell-cell interactions within native tissue environments. The development and evaluation of analytical tools for SRT data necessitate tools for generating synthetic datasets with known ground truth of cell-cell interaction induced features. To address this gap, we introduce sCCIgen, a novel real-data-based simulator tailored to generate high-fidelity SRT data with a focus on cell-cell interactions.
View Article and Find Full Text PDFRisk factors for acquired weakness in intensive care unit patients: An umbrella review.
Intensive Crit Care Nurs
January 2025
Department of Nursing, The First Affiliated Hospital of Kunming Medical University, Kunming, China. Electronic address:
Objective: This umbrella review aims to summarize and synthesize the evidence on risk factors related to intensive care unit-acquired weakness in systematic reviews to create prevention strategies and intervention measures for intensive care unit-acquired weakness.
Methodology: Eight databases were searched systematically from inception to 1st November 2023. Two researchers independently screened and extracted data based on predefined inclusion and exclusion criteria.
Generative dynamical models for classification of rsfMRI data.
Netw Neurosci
December 2024
Department of Psychology, Stanford University, Stanford, CA, USA.
The growing availability of large-scale neuroimaging datasets and user-friendly machine learning tools has led to a recent surge in studies that use fMRI data to predict psychological or behavioral variables. Many such studies classify fMRI data on the basis of static features, but fewer try to leverage brain dynamics for classification. Here, we pilot a generative, dynamical approach for classifying resting-state fMRI (rsfMRI) data.
View Article and Find Full Text PDFRnalib: a Python library for custom transcriptomics analyses.
Bioinformatics
December 2024
Max Perutz Labs, Vienna Biocenter Campus (VBC), Vienna A-1030, Austria.
Motivation: The efficient and reproducible analysis of high-throughput sequencing datasets necessitates the development of methodical and robust computational pipelines that integrate established and bespoke bioinformatics analysis tools, often written in high-level programming languages such as Python. Despite the increasing availability of programming libraries for genomics, there is a noticeable lack of tools specifically focused on transcriptomics. Key tasks in this area include the association of gene features (e.
View Article and Find Full Text PDFInferring gene-pathway associations from consolidated transcriptome datasets: an interactive gene network explorer for .
bioRxiv
December 2024
Department of Pathology, Stanford University School of Medicine, Palo Alto, California, USA.
Although an established model organism, remains comparatively inaccessible to high throughput screens, and alternative bioinformatic approaches still rely on unconnected datasets and outdated algorithms. Here, we report a new approach to consolidating RNA-seq and microarray data based on a systematic exploration of parameters and computational controls, enabling us to infer functional gene associations from their co-expression patterns. To illustrate the power of this approach, we took advantage of new data regarding a previously studied pathway, the biogenesis of a secretory organelle called the mucocyst.
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